Comparison with Yu et al.
In [1]}, Yu et al. use multiple dilated convolutional layers with different dilation rates to extract features at different scales. ADC adopts a similar idea, but implements it in a simple yet efficient way. Firstly, ADC consists of only one convolutional layer. It does not use independent dilated convolutional layers or extra concatenation. Thus ADC is much more computation-economic and time-saving. Secondly, every dilation group in ADC represents features at a different scale, which enables ADC to exploit richer multi-scale information than [1]}. Thirdly, the dilation rates in ADC can be fractional and are adaptively generated, instead of manually set integers. It helps ADC to generalize better to persons of various sizes.
